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Covid‐19‐tested food labels

Author

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  • Longzhong Shi
  • Xuan Chen
  • Bo Chen

Abstract

While the transmission of virus SARS‐CoV‐2 via food is rare, some Chinese food retailers are considering a Covid‐19‐tested food label. However, how consumers may support such a label is unknown. We quantify Chinese consumers’ willingness to pay (WTP) for food carrying a Covid‐19‐tested label using an online choice experiment. We find that the WTPs for such a label are always positive for all food products considered. The amount of WTP depends on the entities authenticating the labels, country of origin of the food, and consumers’ socio‐demographic status. Contrary to expectation, the knowledge on Covid‐19 does not affect consumer preferences for the Covid‐19‐tested food labels. Our benefit and cost analysis suggests a possible large benefit of creating and administering a Covid‐19‐tested food label. This study provides insights for policymakers, global food manufacturers, and retailers to create marketing strategies to alleviate consumer food safety concerns associated with Covid‐19. Alors que la transmission du virus SARS‐CoV‐2 via les aliments est rare, certains détaillants alimentaires chinois envisagent une étiquette alimentaire « Testée Covid‐19 ». Cependant, la façon dont les consommateurs peuvent soutenir une telle étiquette est inconnue. Nous quantifions la capacité à payer (CAP) des consommateurs chinois pour les aliments portant une étiquette « Testée Covid‐19 » à l'aide d'une expérience de choix réalisée en ligne. Nous constatons que les CAP pour un tel label sont toujours positifs pour tous les produits alimentaires considérés. Le montant du CAP dépend des entités authentifiant les étiquettes, du pays d'origine de l'aliment et du statut sociodémographique des consommateurs. Contrairement aux attentes, les connaissances sur le Covid‐19 n'affectent pas les préférences des consommateurs pour les étiquettes alimentaires « Testée Covid‐19 ». Notre analyse des avantages et des coûts suggère un potentiel avantage important pour la création et de l'administration d'une étiquette alimentaire « Testée Covid‐19 ». Cette étude fournit des informations aux décideurs politiques, aux fabricants mondiaux de produits alimentaires et aux détaillants pour créer des stratégies de marketing visant à atténuer les problèmes de sécurité alimentaire des consommateurs associés à la Covid‐19.

Suggested Citation

  • Longzhong Shi & Xuan Chen & Bo Chen, 2023. "Covid‐19‐tested food labels," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 71(2), pages 203-230, June.
  • Handle: RePEc:bla:canjag:v:71:y:2023:i:2:p:203-230
    DOI: 10.1111/cjag.12327
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    References listed on IDEAS

    as
    1. Yang, Yang & Hobbs, Jill E. & Natcher, David C., 2020. "Assessing consumer willingness to pay for Arctic food products," Food Policy, Elsevier, vol. 92(C).
    2. Wu, Linhai & Wang, Shuxian & Zhu, Dian & Hu, Wuyang & Wang, Hongsha, 2015. "Chinese consumers’ preferences and willingness to pay for traceable food quality and safety attributes: The case of pork," China Economic Review, Elsevier, vol. 35(C), pages 121-136.
    3. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    4. John List & Craig Gallet, 2001. "What Experimental Protocol Influence Disparities Between Actual and Hypothetical Stated Values?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 20(3), pages 241-254, November.
    5. Liu, Ruifeng & Gao, Zhifeng & Nayga, Rodolfo M. & Snell, Heather Arielle & Ma, Hengyun, 2019. "Consumers’ valuation for food traceability in China: Does trust matter?," Food Policy, Elsevier, vol. 88(C).
    6. Jerrod M Penn & Wuyang Hu, 2018. "Understanding Hypothetical Bias: An Enhanced Meta-Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(4), pages 1186-1206.
    7. Satoru Shimokawa & Dezhuang Hu & Dandan Li & Hong Cheng, 2021. "The urban–rural gap in the demand for food safety in China: The role of food label knowledge," Agricultural Economics, International Association of Agricultural Economists, vol. 52(2), pages 175-193, March.
    8. Neill, Clinton L. & Williams, Ryan B., 2016. "Consumer Preference For Alternative Milk Packaging: The Case Of An Inferred Environmental Attribute," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 48(3), pages 241-256, August.
    9. Tatiana Drugova & Kynda R. Curtis & Sherzod B. Akhundjanov, 2020. "Are multiple labels on food products beneficial or simply ignored?," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 68(4), pages 411-427, December.
    10. Xiang Wu & Bin Hu & Jie Xiong, 2020. "Understanding Heterogeneous Consumer Preferences in Chinese Milk Markets: A Latent Class Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(1), pages 184-198, February.
    11. Peterson, Hikaru H. & Bernard, John C. & Fox, John A. (Sean) & Peterson, Jeffrey M., 2013. "Japanese Consumers' Valuation of Rice and Pork from Domestic, U.S., and Other Origins," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(1), pages 1-14, April.
    12. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    13. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74, pages 132-132.
    14. Jayson L. Lusk & Ted C. Schroeder & Glynn T. Tonsor, 2014. "Editor's choice Distinguishing beliefs from preferences in food choice," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 41(4), pages 627-655.
    15. Loomis, John B., 2014. "2013 WAEA Keynote Address: Strategies for Overcoming Hypothetical Bias in Stated Preference Surveys," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 39(1), pages 1-13, April.
    16. Liu, Peng & Ma, Liang, 2016. "Food scandals, media exposure, and citizens’ safety concerns: A multilevel analysis across Chinese cities," Food Policy, Elsevier, vol. 63(C), pages 102-111.
    17. Wuyang Hu & Shan Sun & Jerrod Penn & Ping Qing, 2022. "Dummy and effects coding variables in discrete choice analysis," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(5), pages 1770-1788, October.
    18. Junfei Bai & Caiping Zhang & Jing Jiang, 2013. "The role of certificate issuer on consumers’ willingness-to-pay for milk traceability in China," Agricultural Economics, International Association of Agricultural Economists, vol. 44(4-5), pages 537-544, July.
    19. Wongprawmas, Rungsaran & Canavari, Maurizio, 2017. "Consumers’ willingness-to-pay for food safety labels in an emerging market: The case of fresh produce in Thailand," Food Policy, Elsevier, vol. 69(C), pages 25-34.
    20. Jill J. McCluskey & Kristine M. Grimsrud & Hiromi Ouchi & Thomas I. Wahl, 2005. "Bovine spongiform encephalopathy in Japan: consumers' food safety perceptions and willingness to pay for tested beef," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 49(2), pages 197-209, June.
    21. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
    22. Soley, Graham & Hu, Wuyang & Vassalos, Michael, 2019. "Willingness to Pay for Shrimp with Homegrown by Heroes, Community-Supported Fishery, Best Aquaculture Practices, or Local Attributes," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 51(4), pages 606-621, November.
    23. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    24. Andrew A. Goett & Kathleen Hudson & Kenneth E. Train, 2000. "Customers' Choice Among Retail Energy Suppliers: The Willingness-to-Pay for Service Attributes," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 1-28.
    25. Malone, Trey & Lusk, Jayson L., 2017. "Taste Trumps Health And Safety: Incorporating Consumer Perceptions Into A Discrete Choice Experiment For Meat," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 49(1), pages 139-157, February.
    26. Muhammad Zeeshan Zafar & Adnan Maqbool & Lucian-Ionel Cioca & Syed Ghulam Meran Shah & Shahjahan Masud, 2021. "Accentuating the Interrelation between Consumer Intention and Healthy Packaged Food Selection during COVID-19: A Case Study of Pakistan," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    27. Roe, Brian & Teisl, Mario F., 2007. "Genetically modified food labeling: The impacts of message and messenger on consumer perceptions of labels and products," Food Policy, Elsevier, vol. 32(1), pages 49-66, February.
    28. David F. Layton & Gardner Brown, 2000. "Heterogeneous Preferences Regarding Global Climate Change," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 616-624, November.
    29. Ekin Birol & Bhushana Karandikar & Devesh Roy & Maximo Torero, 2015. "Information, Certification and Demand for Food Safety: Evidence from an In-store Experiment in Mumbai," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(2), pages 470-491, June.
    30. Eng, Teck-Yong & Ozdemir, Sena & Michelson, Grant, 2016. "Brand origin and country of production congruity: Evidence from the UK and China," Journal of Business Research, Elsevier, vol. 69(12), pages 5703-5711.
    31. Annika Tienhaara & Heini Ahtiainen & Eija Pouta & Mikołaj Czajkowski, 2022. "Information Use and Its Effects on the Valuation of Agricultural Genetic Resources," Land Economics, University of Wisconsin Press, vol. 98(2), pages 337-354.
    32. Xiang Wu & Bin Hu & Jie Xiong, 2020. "Understanding Heterogeneous Consumer Preferences in Chinese Milk Markets: A Latent Class Approach," Post-Print hal-02489646, HAL.
    33. Linhai Wu & Hongsha Wang & Dian Zhu & Wuyang Hu & Shuxian Wang, 2016. "Chinese consumers’ willingness to pay for pork traceability information—the case of Wuxi," Agricultural Economics, International Association of Agricultural Economists, vol. 47(1), pages 71-79, January.
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